基于深度学习的图书馆个性化推荐系统的研究  

Research on library personalized recommendation system based on deep learning

作　　者：王心妍[1] 陈群英[1] Wang Xinyan;Chen Qunying(Intelligent Science and Information Engineering College,Xi'an Peihua University, Xi’an 710125, China)

机构地区：[1]西安培华学院智能科学与信息工程学院,陕西西安710125

出　　处：《现代科学仪器》2018年第6期44-47,共4页Modern Scientific Instruments

基　　金：西安培华学院2018年度校级科研课题“基于深度学习的图书馆个性化推荐系统的研究”项目(项目编号PHKT18013).

摘　　要：当前图书馆的推荐系统大多数是基于内容的推荐技术和基于协同过滤的推荐技术,但是这些推荐系统忽视了读者个性化信息的深层次特征。为了完善图书管图书推荐系统功能,文章提出了基于受限玻尔兹曼机与协同过滤的图书推荐算法的图书馆推荐系统,并对算法的性能进行仿真实验,结果表明,提出的算法应用效果较好。At present, most recommendation systems in libraries are content-based and collaborative filtering-based, but these recommendation systems ignore the deep-seated characteristics of readers'personalized information. In order to improve the function of Library recommendation system, this paper proposes a library recommendation system based on restricted Boltzmann machine and collaborative filtering algorithm, and simulates the performance of the algorithm. The results show that the proposed algorithm has good application effect.

关 键 词：深度学习 图书馆 个性化推荐系统 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]